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When this happensTriggers
A trigger is an event that starts a workflow.
Triggers when new short url is created.
Action is the task that follows automatically within your TinyURL integrations.
Creates a tiny url.
Compute how closely an image matches a text prompt using Roboflow's CLIP model.
Generate class predictions for an image.
Create instance segmentation predictions (masks and labels) for a given image.
Detect objects in an image and return their labels and bounding boxes.

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Workflow automation is the process of using technology to execute repetitive tasks with minimal human intervention, creating a seamless flow of activities.
To start, connect both your TinyURL and Roboflow accounts to viaSocket. Once connected, you can set up a workflow where an event in TinyURL triggers actions in Roboflow (or vice versa).
Absolutely. You can customize how TinyURL data is recorded in Roboflow. This includes choosing which data fields go into which fields of Roboflow, setting up custom formats, and filtering out unwanted information.
The data sync between TinyURL and Roboflow typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between TinyURL and Roboflow. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
TinyURL is a URL shortening service that allows users to convert long web addresses into shorter, more manageable links. This service is particularly useful for sharing links on social media, in emails, or any platform where space is limited. TinyURL ensures that the shortened links are easy to remember and share, while still directing users to the intended destination.
Learn MoreRoboflow is a leading platform for computer vision and AI model development, offering tools to streamline the process of building, training, and deploying machine learning models.
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